uhf combiner

high frequency (UHF) and microwave. RFID in different frequencies works in different ways. RFID tags in LF and HF frequencies generally use electromagnetic coupling, while RFID in UHF and microwave frequencies generally uses electromagnetic emission. At present, the frequency widely used in the world is distributed in four kinds of bands: low frequency (125 Khz), high frequency (13.54 MHz), and ultra high

In the actual application, when we get a UHF label, often want to know the capacity of its various storage area is how much, especially programmers do the program, it must know what the chip, some characteristics, in order to do a better service for the program, not blind, Otherwise, the development of the program may cause a variety of unforeseen problems. So how do we know what the label is in the hands of the brand, what chip? Here's how to tell us

Brief IntroductionThe role of combiner is to combine the multiple of a map generation into a new one , and then the new one as the input of reduce;There is a combine function between the map function and the reduce function to reduce the intermediate result of the map output, which reduces the data of the map output and reduces the network transmission load ;It is not possible to use Combiner,

ArticleDirectory Declare combiner Function Many mapreduceProgramLimited by the available bandwidth on the cluster, it will try its best to minimize the intermediate data that needs to be transmitted between map and reduce tasks. Hadoop allows you to declare a combiner function to process map output, and use your own map processing result as the reduce input. Because

One: BackgroundIn the MapReduce model, the function of reduce is mostly statistical classification type of total, the maximum value of the minimum value, etc., for these operations can consider the map output after the combiner operation, so as to reduce network transport load, while reducing the burden of reduce tasks . The combiner operation is run on each node, only affects the output of the local map,

As we all know, the hadoop framework uses Mapper to process data into a In the above process, we can see at least two performance bottlenecks: If we have 1 billion million data records, mapper will generate 1 billion key-value pairs for transmission across the network, but if we only calculate the maximum value for the data, obviously, mapper only needs to output the maximum value it knows. This not only reduces the network pressure, but also greatly improves program efficiency. This defini

What is combiner Functions “Many MapReduce jobs are limited by the bandwidth available on the cluster, so it paysto minimize the data transferred between map and reduce tasks. Hadoop allows the user to specify a combiner function to be run on the map output—the combiner function’soutput forms the input to the reduce function. Since the

1.CombinerCombiner is an optimization method for MapReduce. Each map can generate a large amount of local output, and the Combiner function is to merge the output of the map end first to reduce the amount of data transferred between the map and reduce nodes to improve network IO performance. The combiner can be set only if the operation satisfies the binding law.The role of

Partitioner Programming data that has some common characteristics is written to the same file. Sorting and grouping when sorting in the map and reduce phases, the comparison is K2. V2 are not involved in sorting comparisons. If you want V2 to be sorted, you need to assemble K2 and V2 into new classes as K2,To participate in the comparison. If you want to customize the collation, the sorted object is implementedWritablecomparable interface, implementing collations in the CompareTo method

As we all know, the hadoop framework uses Mapper to process data into a In the above process, we can see at least two performance bottlenecks: If we have 1 billion million data records, mapper will generate 1 billion key-value pairs for transmission across the network, but if we only calculate the maximum value for the data, obviously, mapper only needs to output the maximum value it knows. This not only reduces the network pressure, but also greatly improves program efficiency. This defini

1. Concept2. ReferencesImprove the MapReduce job Efficiency Note II of Hadoop (use combiner as much as possible): Http://sishuo (k). com/forum/blogpost/list/5829.htmlHadoop Learning notes -8.combiner and custom Combiner:http://www.tuicool.com/articles/qazujavHadoop in-depth learning: combiner:http://blog.csdn.net/cnbird2008/article/details/23788233(mean Scene) 0Hadoop using

The bandwidth available on the cluster limits the number of mapreduce jobs, so the most important thing to do is to avoid the data transfer between the map task and the reduce task as much as possible. Hadoop allows users to specify a merge function for the output of the map task, and sometimes we also call it combiner, which is like mapper and reducer.The output of the merge function as input to the reduce function, because the merge function is an o

remains the same and the cluster Scale doubles, the computing time is halved. This chapter is arranged as follows: Section 3.1This section describes the importance of local aggregation and details the combiner. Local merge can merge mapper output results to reduce the amount of data that needs to be transmitted over the network. This section describes the "in-mapper combining" design mode. Section 3.2The example of constructing the word co-orrcuranc

3.1 local Aggregation) In a data-intensive distributed processing environment, interaction of intermediate results is an important aspect of synchronization from processes that generate them to processes that consume them at the end. In a cluster environment, except for the embarrassing parallel problem, data must be transmitted over the network. In addition, in hadoop, the intermediate result is first written to the local disk and then sent over the network. Because network and disk factors ar

comment. It is also worth mentioning that snappy, which is developed by Google and open source compression algorithm, is the Cloudera official strongly advocated in mapreduce used in the compression algorithm. It is characterized by: in the case of similar compression rate as the Lzo file, the compression and decompression performance can also be greatly improved, but it is not divisible as a mapreduce input. Extended content: Cloudera Official Blog to snappy Introduction: http://blog.cloudera.

. Therefore, we need to customize partition to choose the record reducer according to our own requirements. Custom Partitioner is simple, as long as you customize a class, and inherit the Partitioner class, overriding its Getpartition method is good, when used by calling the job's setpartitionerclass to specify can beThe results of the map will be distributed to reducer via partition. Mapper results, may send to combiner do merge,

modulation system, the semi-active system label itself is also equipped with a battery, but does not actively send data, the battery is only used to power the internal digital circuit.The main bands of rfid are: 125khz,134.2khz,13.56mhz,860-960 Mhz,2.45ghz and 5.8GHz. The working distance of RFID system with different working frequency is different, and the field of application also varies. Low frequency segment (lf,125khz,134.2khz) RFID system is mainly used for animal identification, facto

How many frequency resources does the Huawei watch mobile phone use? according to the frequency from low to high order, our watch mobile phone use the following frequency resources:1, NFC 13.56mhz--HF;2, FM stereo radio 76~108mhz--very high frequency VHF;3, GPU 900mhz--VHF UHF;4, Beidou navigation 1258~1563mhz--UHF high-frequency;5, GPS navigation 1164~1576mhz--VHF UHF